This browser does not support visualization of term relationships on the Disease Ontology website

Please use Chrome, Safari or Firefox when using the Disease Ontology website to unlock visualization capability

Close notification overlay

Home > Community > Publications

Papers Citing Disease Ontology

The Disease Ontology has been cited by more than 2,100 publications! A graphical representation of publications that cited the DO by year and publication type can be found on the statistics page. To look at individual publications that cited the DO, check out our lens.org collection.

DO Project Publications


To cite your use of the Disease Ontology in your work, please cite one of these publications:

  • Baron, J. A., Johnson, C. S., Schor, M. A., Olley, D., Nickel, L., Felix, V., Munro, J. B., Bello, S. M., Bearer, C., Lichenstein, R., Bisordi, K., Koka, R., Greene, C., & Schriml, L. M. (2024). The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem. Nucleic Acids Research, 52(D1):D1305-D1314. doi: 10.1093/nar/gkad1051; PMID:37953304; PDF.
  • Schriml L. M., Lichenstein R., Bisordi K., Bearer C., Baron J. A., Greene C. (2023). Modeling the enigma of complex disease etiology (2023). Journal of Translational Medicine, 21(1):148. doi: 10.1186/s12967-023-03987-x; PMID:36829165; PDF.
  • Baron J. A., Schriml L. M. (2023). Assessing resource use: a case study with the Human Disease Ontology. Database, 2023:baad007. doi: 10.1093/database/baad007; PMID:36856688; PDF.

  • Schriml, L. M., Munro, J. B., Schor, M., Olley, D., McCracken, C., Felix, V., Baron, J. A., Jackson, R., Bello, S. M., Bearer, C., Lichenstein, R., Bisordi, K., Dialo, N. C., Giglio, M., & Greene, C. (2022). The Human Disease Ontology 2022 update. Nucleic Acids Research, 50(D1):D1255–D1261. doi:10.1093/nar/gkab1063; PMID:34755882; PDF.
  • Schriml, L. M., Mitraka, E., Munro, J., Tauber, B., Schor, M., Nickle, L., Felix, V., Jeng, L., Bearer, C., Lichenstein, R., Bisordi, K., Campion, N., Hyman, B., Kurland, D., Oates, C. P., Kibbey, S., Sreekumar, P., Le, C., Giglio, M., & Greene, C. (2019). Human Disease Ontology 2018 update: Classification, content and workflow expansion. Nucleic Acids Research, 47(D1):D955–D962. doi:10.1093/nar/gky1032; PMID:30407550; PDF.
  • Bello, S. M., Shimoyama, M., Mitraka, E., Laulederkind, S. J. F., Smith, C. L., Eppig, J. T., & Schriml, L. M. (2018). Disease Ontology: Improving and unifying disease annotations across species. Disease Models & Mechanisms, 11(3):dmm032839. doi:10.1242/dmm.032839; PMID:29590633; PDF.
  • Schriml, L. M., & Mitraka, E. (2015). The Disease Ontology: Fostering interoperability between biological and clinical human disease-related data. Mammalian Genome, 26(9):584–589. doi:10.1007/s00335-015-9576-9; PMID:26093607; PDF.
  • Wu, T. J., Schriml, L. M., Chen, Q. R., Colbert, M., Crichton, D. J., Finney, R., Hu, Y., Kibbe, W. A., Kincaid, H., Meerzaman, D., Mitraka, E., Pan, Y., Smith, K. M., Srivastava, S., Ward, S., Yan, C., & Mazumder, R. (2015). Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis. Database, 2015:bav032. doi:10.1093/database/bav032; PMID:25841438; PDF.
  • Kibbe, W. A., Arze, C., Felix, V., Mitraka, E., Bolton, E., Fu, G., Mungall, C. J., Binder, J. X., Malone, J., Vasant, D., Parkinson, H., & Schriml, L. M. (2015). Disease Ontology 2015 update: An expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Research, 43(D1):D1071–D1078. doi:10.1093/nar/gku1011; PMID:25348409; PDF.
  • Schriml, L. M., Arze, C., Nadendla, S., Chang, Y.-W. W., Mazaitis, M., Felix, V., Feng, G., & Kibbe, W. A. (2012). Disease Ontology: A backbone for disease semantic integration. Nucleic Acids Research, 40(D1):D940–D946. doi:10.1093/nar/gkr972; PMID:22080554; PDF.
  • Osborne, J. D., Flatow, J., Holko, M., Lin, S. M., Kibbe, W. A., Zhu, L. (Julie), Danila, M. I., Feng, G., & Chisholm, R. L. (2009). Annotating the human genome with Disease Ontology. BMC Genomics, 10(Suppl 1):S6. doi:10.1186/1471-2164-10-S1-S6; PMID:19594883; PDF.
  • Du, P., Feng, G., Flatow, J., Song, J., Holko, M., Kibbe, W. A., & Lin, S. M. (2009). From disease ontology to disease-ontology lite: Statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations. Bioinformatics, 25(12):i63–i68. doi:10.1093/bioinformatics/btp193; PMID:19478018; PDF.

DO Collaborative Publications

  • CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase (2023). PMID:36373660
  • Diseasomics: Actionable machine interpretable disease knowledge at the point-of-care (2022). PMID:36812614
  • A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology (2022). PMID:36271389
  • A community approach to the cancer-variant-interpretation bottleneck (2022). PMID:35624339
  • COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model (2021). PMID:34015823
  • Wikidata as a knowledge graph for the life sciences (2020). PMID:32180547
  • ECO, the Evidence & Conclusion Ontology: community standard for evidence information (2019). PMID:30407590
  • FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration (2018). PMID:31304272
  • Wikidata as a semantic framework for the Gene Wiki initiative (2016). PMID:26989148
  • The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease (2015). PMID:26119816
  • Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis (2015). PMID:25841438
  • GeMInA, Genomic Metadata for Infectious Agents, a geospatial surveillance pathogen database (2010). PMID:19850722
Top